Prediction of Diabetes Complications Using Computational Intelligence Techniques

Abstract

Diabetes is a complex disease that can lead to serious health complications if left unmanaged. Early detection and treatment of diabetes is crucial, and data analysis and predictive techniques can play a significant role. Data mining techniques, such as classification and prediction models, can be used to analyse various aspects of data related to diabetes, and extract useful information for early detection and prediction of the disease. XGBoost classifier is a machine learning algorithm that effectively predicts diabetes with high accuracy. This algorithm uses a gradient-boosting framework and can handle large and complex datasets with high-dimensional features. However, it is important to note that the choice of the best algorithm for predicting diabetes may depend on the specific characteristics of the data and the research question being addressed. In addition to predicting diabetes, data analysis and predictive techniques can also be used to identify risk factors for diabetes and its complications, monitor disease progression, and evaluate the effectiveness of treatments. These techniques can provide valuable insights into the underlying mechanisms of the disease and help healthcare providers make informed decisions about patient care. Data analysis and predictive techniques have the potential to significantly improve the early detection and management of diabetes, a fast-growing chronic disease that notable health hazards. The XGBoost classifier showed the most effectiveness, with an accuracy rate of 89%.

Utilizing Artificial Intelligence Among Patients With Diabetes: A Systematic Review and Meta-Analysis

“…Additionally, research carried out by Alghamdi supported these findings and revealed that ANN models perform better than the other models in terms of sensitivity and accuracy, thus demonstrating the usefulness of ANNs as a method for predicting diabetes complications. However, they saw that it is crucial to choose the most appropriate method based on the specific requirements of treatments [ 16 ]. More so, these findings in a study by Alfian et al indicated that the recommended ANN-based blood glucose prediction model outperformed all other models.…”

Early Prediction of Gestational Diabetes with Parameter-Tuned K-Nearest Neighbor Classifier

“…The PIDD dataset is one of the standard datasets previously employed in several studies [34], [35], [36], [37] for the development of gestational diabetes prediction, prognosis, and diagnosis with machine learning algorithms. The dataset is collected from the online Kaggle repository available at the following link which if previously employed by the study [38], [39], [40] can be downloaded at https://www.kaggle.com/datasets/uciml/pima-indiansdiabetes-database. There were 768 instances each with 8 features.…”

Evaluation of sequential feature selection in improving the K-nearest neighbor classifier for diabetes prediction

“…As preventative and treatment measures, MLM has been widely researched and different models have been developed. For instance, Alghamdi [2] has developed an extreme gradient boosting (XGBoost) model for the early prediction of diabetes. The evaluation of the performance of the XGBoost showed that the model achieved an accuracy of 89% for predicting diabetes disease at an early stage.…”